Tag Archives: Remote Sensing Sensors

GUEST POST: Challenges – Geometric Correction of Optical High Resolution Satellite Imaging

This blog post is in continuation of a previous post i.e. “A Very Brief History of Optical High Resolution Satellite Imaging”. For laymen, high resolution satellite images are fascinating as mere pictures containing earth features. But for the remote sensing analysts or experts, challenges start to come up while trying to quantify earth features through image processing algorithms. To attain accurate quantitative results from high resolution satellite images is subject to positional accuracy. If the images are not observed from exactly the same point in space, then they can have different displacements, which could cause geo-registration errors. Geometric or ortho-rectification (especially in mountain areas) of the satellite images is vital to overcome the distortions related to the sensor (e.g. jitter, view angle effects), satellite (e.g. attitude deviations from nominal), and Earth (e.g. rotation, curvature, relief).

All high resolution optical Earth Observation (EO) satellites are equipped with global navigation satellite systems (such as GPS), star sensors, and gyroscopes. Although most high resolution imaging sensors provide high resolution digital elevation models (DEMs) along with satellite images for accurate ortho-rectification, but due to cost factor of high resolution DEMs, analysts often prefer to rely on publicly freely available DEM data with the integration of Rational Polynomial Coefficient (RPC) files. The sources of distortion can be grouped into two broad categories: the Observer or the acquisition system (platform, imaging sensor and measuring instruments, such as gyroscope, stellar sensors, etc.) and the Observed (atmosphere and Earth). Factors such as sensor view angle, sun elevation and topography have a significant effect on the geometric properties of high resolution image (see table 1).

Table 1: Description of sources of error for the two categories, the Observer and the Observed, with the different sub-categories

Category Sub category Description of error sources
The Observer Platform (spaceborne or airborne) Variation of the movement
Variation in platform attitude (low to high frequencies)
Sensor Variation in sensor mechanics (scan rate, scanning velocity, etc.)
Viewing/look angles
Panoramic effect with field of view
Measuring instruments Time-variations or drift
Clock synchronicity
The Observed Atmosphere Refraction and turbulence
Earth Curvature, rotation, topographic effect
Map Geoid to ellipsoid
Ellipsoid to map

Algorithms

For geo-referencing or ortho-rectification of satellite images, several commercial and non-commercial algorithms are available. ERDAS Imagine, ENVI-IDL, PCI Geomatics, IDRISI, ESRI ArcMap and ArcView, Global Mapper etc. are most common and well known commercial softwares while GRASS, QGIS (Quantum GIS), PostGIS, uDig, gvSIG, etc. are open source softwares. Restore 1.0 software can perform image band operations, mathematical image calculations, bundle adjustment with self-calibration, image transformations, image enhancement, filter operations and rectification of any digital image. AutoGR Toolkit can perform automatic matching (scale, rotation and even color invariant) and geo-referencing in few seconds.. For time-saving and fast output products, batch processes through supercomputing technology are being implemented.

Example

This example is based on GeoEye-1 (0.5 m resolution) satellites images of Dolakha District, Nepal. Two adjacent images were captured on 2nd November, 2009, with 25.4° off-nadir view angle having 40% overlay area. When both images were ortho-rectified using RPC and 20m topographic DEM, a huge displacement with no data between the images was observed (see Figure 1) with irregular shapes of tree crowns (see Figure 2). This distorted area (irregular shape) occurred in small patches and its distribution was not systematic. For example, in some places where there were less steep slopes (up to 40°), distorted parts did not occur. While processing high resolution satellite images, similarly you may find out geometric distortions.

Figure 1: GeoEye-1 image before ortho-rectification (on the left) and after ortho-rectification (on the right) through RPC files and 20m DEM.

Figure 2: Irregular shaped tree crowns due to off-nadir view angle, before (on the left) and after ortho-rectification (on the right).

Conflicts of Interest: The findings reported stand as scientific study and observations of the author and do not necessarily reflect as the views of author’s organizations.

About this post: This is a guest post by Hammad Gilani. Learn more about this blog’s authors here

GUEST POST: A Very Brief History of Optical High Resolution Satellite Imaging

The history of the optical high resolution satellite images starts from classified military satellite systems of the United States of America that captured earth’s surface from 1960 to 1972. All these images were declassified by Executive Order 12951 in 1995 and made publically available (Now freely available through the USGS EarthExplorer data platform under the category of declassified data). From 1999 onward, commercial multispectral and panchromatic datasets have been available for public. Launch of Keyhole Earthviewer in 2001, later renamed as Google Earth in 2005, opened a new avenue for the layman to visualize earth features through optical high resolution satellite images.

A comparison of declassified Corona (1974) vs. GeoEye-1 (2014) image. Image credits: EarthExplorer (Corona) and Google Earth (GeoEye-1).

In the current era, most high resolution satellite images are commercially available, and are being used as a substitute to aerial photographs. The launch of SPOT, IKONOS, QuickBird, OrbView, GeoEye, WorldView, KOMPSAT etc. offer data at fine resolutions in digital format to produce maps in much simpler, cost effective and efficient manner in terms of mathematical modeling. A number of meaningful products are being derived from high resolution datasets, e.g., extraction of high resolution Digital Elevation Models (DEMs) with 3D building models, detailed change assessments of land cover and land use, habitat suitability, biophysical parameters of trees, detailed assessments of pre and post-disaster conditions, among others.

Both aerial photographs and high resolution images are subject to weather conditions but satellites offer the advantage of repeatedly capturing same areas on a reliable basis by considering the user demand without being restricted by considering borders and logistics, as compared to aerial survey.

Pansharpening / resolution merge provides improved visualization and is also used for detecting certain features in a better manner. Pansharpening / resolution merge is a fusion process of co-georegistered panchromatic (high resolution) and multispectral (comparatively lower resolution) satellite data to produce high-resolution color multispectral image. In high resolution satellite data, the spectral resolution is being increased and more such sensors with enhanced spectral sensitivity are being planned in the future.

List of the Spaceborne Sensors with <5 m Spatial Resolution

Sensors Agency/Country Launch Date Platform altitude (km) GSD Pan/MSS (m) Pointing capability (o) Swath width at nadir (km)
IKONOS-2 GeoEye Inc./USA 1999 681 0.82/3.2 Free View 11.3
EROS A1 ImageSat Int./Cyprus (Israel) 2000 480 1.8 Free View 12.6
QuickBird DigitalGlobe/USA 2001 450 0.61/2.44 Pan and MSS alternative Free View 16.5
HRS SPOT Image/France 2002 830 5X10 Forward/left +20/-20 120
HRG SPOT Image/France 2002 830 5(2.5)x10 sideways up to ±27 60
OrbViw-3 GeoEye Inc./USA 2003 470 1/4 Free View 8
FORMOSAT 2 NSPO/China, Taiwan 2004 890 2/8 Free View 24
PAN (Cartosat-1) ISRO/India 2005 613 2.5 Forward/aft 26/5 Free view to side up to 23 27
TopSat Telescope BNSC/UK 2005 686 2.8/5.6 Free View 15/10
PRISM JAXA/Japan 2005 699 2.5 Forward/Nadir/aft -24/0/+24 Free view to side 70 35 (Triplet stereo observations
PAN(BJ-1) NRSCC (CAST)/China 2005 686 4/32 Free View 24/640
EROS B ImageSat Int./Cyprus (Israel) 2006 508 0.7/- Free View 7
Geoton-L1Resurs-DK1 Roscosmos/Russia 2006 330-585 1/3 for h = 330km Free View 30 for h = 330km
KOMPSAT-2 KARI/South Korea 2006 685 1/4 sideways up to ±30 15 km
CBERS-2B CNSA/INPE China/Brazil 2007 778 2.4/20 Free View 27/113
WorldView-1 DigitalGlobe/USA 2007 494 0.45/- Free View 17.6
THEOS GISTDA/Thailand 2008 822 2/15 Free View 22/90
AlSat-2 Algeria 2008 680 2.5 up to 30 cross track Free view 17.5
GeoEye-1 GeoEye Inc./USA 2008 681 0.41/1.65 Free View 15.2
WorldView-2 DigitalGlobe/USA 2009 770 0.45/1.8 Free View 16.4
PAN (Cartosat-2, 2A, 2B) ISRO/India Cartosat 2-2007 Cartosat 2A-2008 Cartosat   2B-2010 631 0.82/- Free View 9.6
KOMPSAT-3 KARI/South Korea 2012 685 0.7/2.8 ±45º into any direction (cross-track or along-track) 15
WorldView-3 DigitalGlobe/USA 2014 617 0.3/1.24/3.7/30 13.1

 Conflicts of Interest: The findings reported stand as scientific study and observations of the author and do not necessarily reflect as the views of author’s organizations.

 About this post: This is a guest post by Hammad Gilani. Learn more about this blog’s authors here